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Machine Learning, Statistical Software, and Data-Driven Analytics (Table 32)
Fri, April 12, 4:55 to 6:25pm, Pennsylvania Convention Center, Floor: Level 200, Exhibit Hall BSession Type: Roundtable Session
Abstract
Papers at this roundtable session focus on methodological innovations with machine learning and large-scale assessment data, simulated classrooms to train pre-service teachers, and updates on statistical software and use of sampling weights and multilevel models.
Sub Unit
Chair
Papers
A Comparison of Machine Learning Methods to Estimate Conditional Treatment Effect With Multilevel Observational Data - Jia Quan, University of Kansas; Walter L. Leite, University of Florida; Wei Li, University of Florida
Applying Natural Language Processing to Support Peer Interactions in Multi-Speaker Science Discussions - Michael John Ilagan, McGill University; Jamie N. Mikeska, Educational Testing Service; Beata Beigman Kelbanov, Educational Testing Service
Comparing Software Programs in Conducting Model Analysis With Sampling Weights - Ting Shen, Missouri University of Science & Technology; Ya Zhang, Western Michigan University
Development of an Online Scale Linking Design Using Random Item Selection and Content Balancing Procedures - Chi-Chen Chen, National Academy for Educational Research - Taiwan; Jyun-Hong Chen, National Cheng Kung University; Ching-Lin Shih, National Sun Yat-Sen University
Handling Missing Value: Imputation and the Alternatives - Yi Lu, The Federation of State Boards of Physical Therapy; Yu Zhang, The Federation of State Boards of Physical Therapy; Lorin Mueller, FSBPT